Solving cell formation problem in cellular manufacturing using ant-colony-based optimization

Grouping the machines and parts in a cellular manufacturing system based on similarities is known as the cell formation problem. It has been shown that cell formation problem is a NP-hard problem. In this paper, the ant colony optimization (ACO) method is used as an evolutionary approach to solve the cell formation problem. This model uses a P = [Pij] (C) × (M + P) pheromone matrix in which C, M, and P are the number of cells, machines, and parts, respectively. In order to represent the sequence of operations, the machine–part incidence matrix entries are considered as positive integers. Performance of the proposed algorithm is tested on some benchmark problems existing in the literature to show the applicability and effectiveness of the proposed model. Comparison of the solutions obtained by the proposed algorithm with those reported in the literature indicates that application of the proposed algorithm has resulted in 5.73% improvement in the total number of intercellular movements and voids on average.